{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:M23TO45JZX3ZROJ2L7HOZHZDF6","short_pith_number":"pith:M23TO45J","schema_version":"1.0","canonical_sha256":"66b73773a9cdf798b93a5fceec9f232f871b6039617e2257e593be63176be2d2","source":{"kind":"arxiv","id":"2505.17500","version":1},"attestation_state":"computed","paper":{"title":"The Discovery Engine: A Framework for AI-Driven Synthesis and Navigation of Scientific Knowledge Landscapes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cond-mat.soft","authors_text":"Andrew Pashea, Austin Cook, Benedikt Waldeck, Daniel Friedman, Janna Lumiruusu, Shagor Rahman, Vladimir Baulin","submitted_at":"2025-05-23T05:51:34Z","abstract_excerpt":"The prevailing model for disseminating scientific knowledge relies on individual publications dispersed across numerous journals and archives. This legacy system is ill suited to the recent exponential proliferation of publications, contributing to insurmountable information overload, issues surrounding reproducibility and retractions. We introduce the Discovery Engine, a framework to address these challenges by transforming an array of disconnected literature into a unified, computationally tractable representation of a scientific domain. Central to our approach is the LLM-driven distillation"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2505.17500","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.soft","submitted_at":"2025-05-23T05:51:34Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"021e84397c198ad1e06700d08c38b487dc53e0ca55f34bcf1d2b427e62f08a8c","abstract_canon_sha256":"95cdd1da9b3387135c2ad74d29d50240314e6d1d4abcb9de41d07092be960eeb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:08:27.163039Z","signature_b64":"OmsJRZnxoFFTGhHC4BmeJDYao1kwJIi43jj5pKpTc68E5GXferNL86YRmwn0gJKCZtRXN3MxsTH6vr8IYkzqDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"66b73773a9cdf798b93a5fceec9f232f871b6039617e2257e593be63176be2d2","last_reissued_at":"2026-07-05T11:08:27.162613Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:08:27.162613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Discovery Engine: A Framework for AI-Driven Synthesis and Navigation of Scientific Knowledge Landscapes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cond-mat.soft","authors_text":"Andrew Pashea, Austin Cook, Benedikt Waldeck, Daniel Friedman, Janna Lumiruusu, Shagor Rahman, Vladimir Baulin","submitted_at":"2025-05-23T05:51:34Z","abstract_excerpt":"The prevailing model for disseminating scientific knowledge relies on individual publications dispersed across numerous journals and archives. This legacy system is ill suited to the recent exponential proliferation of publications, contributing to insurmountable information overload, issues surrounding reproducibility and retractions. We introduce the Discovery Engine, a framework to address these challenges by transforming an array of disconnected literature into a unified, computationally tractable representation of a scientific domain. Central to our approach is the LLM-driven distillation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.17500","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2505.17500/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2505.17500","created_at":"2026-07-05T11:08:27.162664+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.17500v1","created_at":"2026-07-05T11:08:27.162664+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.17500","created_at":"2026-07-05T11:08:27.162664+00:00"},{"alias_kind":"pith_short_12","alias_value":"M23TO45JZX3Z","created_at":"2026-07-05T11:08:27.162664+00:00"},{"alias_kind":"pith_short_16","alias_value":"M23TO45JZX3ZROJ2","created_at":"2026-07-05T11:08:27.162664+00:00"},{"alias_kind":"pith_short_8","alias_value":"M23TO45J","created_at":"2026-07-05T11:08:27.162664+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.01489","citing_title":"SciResearcher: Scaling Deep Research Agents for Frontier Scientific Reasoning","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/M23TO45JZX3ZROJ2L7HOZHZDF6","json":"https://pith.science/pith/M23TO45JZX3ZROJ2L7HOZHZDF6.json","graph_json":"https://pith.science/api/pith-number/M23TO45JZX3ZROJ2L7HOZHZDF6/graph.json","events_json":"https://pith.science/api/pith-number/M23TO45JZX3ZROJ2L7HOZHZDF6/events.json","paper":"https://pith.science/paper/M23TO45J"},"agent_actions":{"view_html":"https://pith.science/pith/M23TO45JZX3ZROJ2L7HOZHZDF6","download_json":"https://pith.science/pith/M23TO45JZX3ZROJ2L7HOZHZDF6.json","view_paper":"https://pith.science/paper/M23TO45J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.17500&json=true","fetch_graph":"https://pith.science/api/pith-number/M23TO45JZX3ZROJ2L7HOZHZDF6/graph.json","fetch_events":"https://pith.science/api/pith-number/M23TO45JZX3ZROJ2L7HOZHZDF6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M23TO45JZX3ZROJ2L7HOZHZDF6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M23TO45JZX3ZROJ2L7HOZHZDF6/action/storage_attestation","attest_author":"https://pith.science/pith/M23TO45JZX3ZROJ2L7HOZHZDF6/action/author_attestation","sign_citation":"https://pith.science/pith/M23TO45JZX3ZROJ2L7HOZHZDF6/action/citation_signature","submit_replication":"https://pith.science/pith/M23TO45JZX3ZROJ2L7HOZHZDF6/action/replication_record"}},"created_at":"2026-07-05T11:08:27.162664+00:00","updated_at":"2026-07-05T11:08:27.162664+00:00"}